RRepoGEO

REPOGEO REPORT · LITE

Agent-Field/SWE-AF

Default branch main · commit ebf00a35 · scanned 6/16/2026, 6:37:14 PM

GitHub: 868 stars · 140 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Agent-Field/SWE-AF, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's main heading to clarify its purpose as an autonomous engineering team that ships code

    Why:

    CURRENT
    # SWE-AF
### Autonomous Engineering Team Runtime Built on AgentField
    COPY-PASTE FIX
    # SWE-AF: Autonomous Software Engineering Fleet
## Ship Production-Grade Code & PRs with an AI Engineering Team
  • mediumreadme#2
    Add a 'What SWE-AF Is (and Isn't)' section to explicitly differentiate from code assistants and clarify its primary function

    Why:

    COPY-PASTE FIX
    ## What SWE-AF Is (and Isn't)

SWE-AF is an autonomous software engineering fleet designed to take a high-level goal and deliver production-grade code and pull requests end-to-end. It orchestrates a full team of AI agents (product managers, architects, coders, testers) to plan, build, and ship software.

**SWE-AF is NOT:**
*   **Just a code assistant or generator:** Unlike tools like GitHub Copilot or Cursor, SWE-AF manages the entire software development lifecycle, from planning to shipping, not just code completion or generation.
*   **Primarily a benchmark:** While SWE-AF includes a benchmark for evaluating agent performance, its core purpose is to *implement* and *ship* software autonomously, serving as a working engineering team.
*   **A testing-only tool:** Unlike Applitools or Testim.io, testing is just one phase within SWE-AF's comprehensive development process.
  • lowtopics#3
    Enhance repository topics with terms related to full lifecycle automation and production-ready code

    Why:

    CURRENT
    agent-factory, agentfield, agentic-ai, ai-agents, ai-factory, autonomous-agents, claude-code, code-generation, codex, coding-agent, developer-tools, gemini-cli, llm, multi-agent, open-source, openrouter, python, software-engineering
    COPY-PASTE FIX
    agent-factory, agentfield, agentic-ai, ai-agents, ai-factory, autonomous-agents, claude-code, code-generation, codex, coding-agent, developer-tools, gemini-cli, llm, multi-agent, open-source, openrouter, python, software-engineering, sdlc-automation, production-ready-code, end-to-end-development, full-stack-ai

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Agent-Field/SWE-AF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 1×
  2. Cursor · recommended 1×
  3. Code Llama (Meta) · recommended 1×
  4. Applitools Eyes · recommended 1×
  5. Testim.io · recommended 1×
  • CATEGORY QUERY
    How can I automate the entire software development lifecycle using AI agents?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Cursor
    3. Code Llama (Meta)
    4. Applitools Eyes
    5. Testim.io
    6. Mabl
    7. OpsRamp
    8. Dynatrace
    9. Prow (Kubernetes) (kubernetes/test-infra)
    10. ChatGPT/GPT-4 (OpenAI)
    11. Midjourney
    12. DALL-E 3
    13. Jira
    14. GitHub Issues
    15. LangChain (langchain-ai/langchain)
    16. AutoGen (microsoft/autogen)
    17. Auto-GPT (Significant-Gravitas/Auto-GPT)
    18. BabyAGI (yoheinakajima/babyagi)

    AI recommended 18 alternatives but never named Agent-Field/SWE-AF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tool to generate production-ready code and pull requests with AI assistance.
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. CodiumAI (Codium-ai/pr-agent)
    3. Cursor (getcursor/cursor)
    4. Tabnine (tabnine/tabnine-vscode)
    5. JetBrains AI Assistant
    6. Codeium (Codeium/Codeium)

    AI recommended 6 alternatives but never named Agent-Field/SWE-AF. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Agent-Field/SWE-AF?
    pass
    AI named Agent-Field/SWE-AF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Agent-Field/SWE-AF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Agent-Field/SWE-AF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Agent-Field/SWE-AF solve, and who is the primary audience?
    pass
    AI named Agent-Field/SWE-AF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Agent-Field/SWE-AF — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite